Unraveling implicit knowledge in information technology jobs

UNCG Author/Contributor (non-UNCG co-authors, if there are any, appear on document)
Prashant Palvia, Joe Rosenthal Excellence Professor and Director of the McDowell Research Center for Global IT Management (Creator)
The University of North Carolina at Greensboro (UNCG )
Web Site: http://library.uncg.edu/

Abstract: Job seekers are used to looking at job postings published on the main websites like Glassdoor and Google Jobs. Typically, an online job posting provides a piece of text that describes the job in a more qualitative way. Most job seekers, who would have to view hundreds of postings every day, tend to pay attention to the explicit information exposed by the textual description, such as required skills, salary, and benefits, which are information that the author wishes to convey to the job seekers directly. However, this would lead to overlook of a large part of implicit information which is hidden deeper in the linguistic characteristics of the textual description, such as readability of the text, status of the employer, and domain-unrelated concerns of the text. These implicit aspects of the job description can give job seekers knowledge into the job culture and personal characteristics of future colleagues, helping them to prepare for job interviews more efficiently, and integrate into future job environment more smoothly. Using text mining methods, this study extracts various types of implicit information/knowledge of a collection of more than 24 thousand job postings and depicts the implicit characteristics of IT-related jobs compared to non-IT jobs. Analysis results show that IT-related and non-IT job descriptions have distinct profiles in terms of implicit characteristics.

Additional Information

25th Americas Conference on Information Systems, AMCIS 2019
Language: English
Date: 2019
IT-related jobs, implicit knowledge, non-IT jobs, job postings, text mining, readability, subjectivity, sentiment, emotion, speech act

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